Mining Frequent Closed Sequential Patterns Using the TriBackClo Algorithm (SPMF documentation)

This example explains how to run the TriBackClo algorithm using the SPMF open-source data mining library.

How to run this example?

What is TriBackClo?

TriBackClo (2026) is an algorithm for discovering frequent closed sequential patterns in sequence databases, implemented in SPMF. This implementation was contributed by Nabil Azizi et al.

What is the input of TriBackClo?

The input of TriBackClo is a sequence database and a user-specified threshold named minsup given as an integer (the minimum number of sequences that must contain a pattern).

A sequence database is a set of sequences where each sequence is a list of itemsets. An itemset is an unordered set of items. For example, the table shown below contains four sequences. The first sequence, named S1, contains 5 itemsets. It means that item 1 was followed by items 1 2 and 3 at the same time, which were followed by 1 and 3, followed by 4, and followed by 3 and 6. It is assumed that items in an itemset are sorted in lexicographical order. This database is provided in the file "contextPrefixSpan.txt" of the SPMF distribution. Note that it is assumed that no items appear twice in the same itemset and that items in an itemset are lexically ordered.

ID Sequences
S1 (1), (1 2 3), (1 3), (4), (3 6)
S2 (1 4), (3), (2 3), (1 5)
S3 (5 6), (1 2), (4 6), (3), (2)
S4 (5), (7), (1 6), (3), (2), (3)

What is the output of TriBackClo?

TriBackClo discovers all frequent closed sequential patterns that occur in a sequence database.

To explain more formally what is a closed sequential pattern, it is necessary to review some definitions.

A sequential pattern is a sequence. A sequence SA = X1, X2, ... Xk, where X1, X2... Xk are itemsets, is said to occur in another sequence SB = Y1, Y2, ... Ym, where Y1, Y2... Ym are itemsets, if and only if there exists integers 1 <= i1 < i2... < ik <= m such that X1 ⊆ Yi1, X2 ⊆ Yi2, ... Xk ⊆ Yik.

The support of a sequential pattern is the number of sequences where the pattern occurs divided by the total number of sequences in the database. In this implementation, the output uses the absolute support (number of sequences).

A frequent sequential pattern is a sequential pattern having a support no less than the minsup parameter provided by the user.

A closed sequential pattern is a frequent sequential pattern such that it is not strictly included in another frequent pattern having the same support.

Why using TriBackClo? The set of closed sequential patterns is generally much smaller than the set of all frequent sequential patterns, while preserving the support information.

Optional parameter(s)

The TriBackClo implementation allows to specify additional optional parameter(s):

These parameter(s) are available in the GUI of SPMF and also in the example(s) "MainTestTriBackClo... .java" provided in the source code of SPMF.

The parameter(s) can also be used in the command line with the Jar file. If you want to use these optional parameter(s) in the command line, it can be done as follows. Consider this example:
java -jar spmf.jar run TriBackClo contextPrefixSpan.txt output.txt 2 true true false
 This command means to apply the algorithm on the file "contextPrefixSpan.txt" and output the results to "output.txt". Moreover, it specifies that the user wants to find patterns for minsup = 2 sequences, using subtree pruning and node gating, and with eager verification disabled.

Input file format

The input file format is defined as follows. It is a text file where each line represents a sequence from a sequence database. Each item from a sequence is a positive integer and items from the same itemset within a sequence are separated by single space. Note that it is assumed that items within a same itemset are sorted according to a total order and that no item can appear twice in the same itemset. The value "-1" indicates the end of an itemset. The value "-2" indicates the end of a sequence (it appears at the end of each line). For example, the input file "contextPrefixSpan.txt" contains the following four lines (four sequences).

@FILETYPE="Simple sequence database"
@SOURCE="SPMF SOFTWARE https://philippe-fournier-viger.com/spmf/"
1 -1 1 2 3 -1 1 3 -1 4 -1 3 6 -1 -2
1 4 -1 3 -1 2 3 -1 1 5 -1 -2
5 6 -1 1 2 -1 4 6 -1 3 -1 2 -1 -2
5 -1 7 -1 1 6 -1 3 -1 2 -1 3 -1 -2

The first two lines are optional metadata. The first line indicate the type of file and the second line describes the source of the data. Then it is the data section. The first line of data represents a sequence where the itemset {1} is followed by the itemset {1, 2, 3}, followed by the itemset {1, 3}, followed by the itemset {4}, followed by the itemset {3, 6}. The next lines follow the same format.

Note that it is also possible to use a text file containing a text (several sentences) if the text file has the ".text" extension, as an alternative to the default input format. If the algorithm is applied on a text file from the graphical interface or command line interface, the text file will be automatically converted to the SPMF format, by dividing the text into sentences separated by ".", "?" and "!", where each word is considered as an item. Note that when a text file is used as input of a data mining algorithm, the performance will be slightly less than if the native SPMF file format is used because a conversion of the input file will be automatically performed before launching the algorithm and the result will also have to be converted. This cost however should be small.

Output file format

The output file format is defined as follows. It is a text file. Each line is a frequent closed sequential pattern. Each item from a sequential pattern is a positive integer and items from the same itemset within a sequence are separated by single spaces. The value "-1" indicates the end of an itemset. On each line, the sequential pattern is first indicated. Then, the keyword " #SUP: " appears followed by an integer indicating the support of the pattern as a number of sequences. For example, a few lines from an output file are shown below:

1 2 -1 4 -1 3 -1 #SUP: 2
1 2 -1 6 -1 #SUP: 2
1 -1 2 -1 3 -1 #SUP: 2

The first line indicates that the frequent closed sequential pattern consisting of the itemset {1, 2}, followed by the itemset {4}, followed by the itemset {3} has a support of 2 sequences. The next lines follow the same format.

Optional feature: giving names to items

Some users have requested the feature of giving names to items instead of using numbers. This feature is offered in the user interface of SPMF and in the command line of SPMF. To use this feature, your file must include @CONVERTED_FROM_TEXT as first line and then several lines to define the names of items in your file. For example, consider the example database "contextPrefixSpan.txt". Here we have modified the file to give names to the items:

@CONVERTED_FROM_TEXT
@FILETYPE="Simple sequence database"
@SOURCE="SPMF SOFTWARE https://philippe-fournier-viger.com/spmf/"
@ITEM=1=apple
@ITEM=2=orange
@ITEM=3=tomato
@ITEM=4=milk
@ITEM=5=bread
@ITEM=6=noodle
@ITEM=7=rice
@ITEM=-1=|
1 -1 1 2 3 -1 1 3 -1 4 -1 3 6 -1 -2
1 4 -1 3 -1 2 3 -1 1 5 -1 -2
5 6 -1 1 2 -1 4 6 -1 3 -1 2 -1 -2
5 -1 7 -1 1 6 -1 3 -1 2 -1 3 -1 -2

In this file, the first line indicates, that it is a file where names are given to items. Then, the second and third line indicate the type of file and the source of the data. Then, the fourth line indicates that the item 1 is called "apple". The fifth line indicates that the item 2 is called "orange", and so on. The 11th line indicates that the symbol "-1" must be replaced by "|". Then the following lines define four sequences in the SPMF format.

Then, if we apply a sequential pattern mining algorithm using this file using the user interface of SPMF or the command line, the output file contains several patterns having this format:

apple | orange | #SUP: 4

Note that this feature could be also used from the source code of SPMF using the ResultConverter class. However, there is currently no example provided.

Performance

TriBackClo is a very efficient algorithm and this is the original version provided by the authors. The algorithm offers optimization options (subtree pruning, node gating, eager verification).

Where can I get more information about this algorithm?

More information about TriBackClo can be found in the original paper:

Nabil Azizi, Makhlouf Ledmi, Abdeldjalil Ledmi, Mohammed El Habib Souidi, Mohamed Boussalem, and Aboubekeur Hamdi-Cherif, "TriBack-clo: Sound triple-witness BackScan for closed pattern mining in itemset-sequences", Information Sciences, 2026, Article 123788. DOI: https://doi.org/10.1016/j.ins.2026.123788

Besides, you may read this survey of sequential pattern mining, which gives an overview of sequential pattern mining algorithms.

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